Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.
Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.
Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Increasingly it is also being used for quantitative research studies of brain disease and psychiatric illness. Neuroimaging is highly multidisciplinary involving neuroscience, computer science, psychology and statistics, and is not a medical specialty. Neuroimaging is sometimes confused with neuroradiology.
Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.
In neuroscience, the default mode network (DMN), also known as the default network, default state network, or anatomically the medial frontoparietal network (M-FPN), is a large-scale brain network primarily composed of the dorsal medial prefrontal cortex, posterior cingulate cortex, precuneus and angular gyrus. It is best known for being active when a person is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming and mind-wandering. It can also be active during detailed thoughts related to external task performance. Other times that the DMN is active include when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future.
The Human Connectome Project (HCP) is a five-year project sponsored by sixteen components of the National Institutes of Health, split between two consortia of research institutions. The project was launched in July 2009 as the first of three Grand Challenges of the NIH's Blueprint for Neuroscience Research. On September 15, 2010, the NIH announced that it would award two grants: $30 million over five years to a consortium led by Washington University in St. Louis and the University of Minnesota, with strong contributions from University of Oxford (FMRIB) and $8.5 million over three years to a consortium led by Harvard University, Massachusetts General Hospital and the University of California Los Angeles.
Mark Steven Cohen is an American neuroscientist and early pioneer of functional brain imaging using magnetic resonance imaging. He currently is a Professor of Psychiatry, Neurology, Radiology, Psychology, Biomedical Physics and Biomedical Engineering at the Semel Institute for Neuroscience and Human Behavior and the Staglin Center for Cognitive Neuroscience. He is also a performing musician.
Resting state fMRI is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed. A number of resting-state brain networks have been identified, one of which is the default mode network. These brain networks are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI.
The following outline is provided as an overview of and topical guide to brain mapping:
Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.
Bruce Rosen is an American physicist and radiologist and a leading expert in the area of functional neuroimaging. His research for the past 30 years has focused on the development and application of physiological and functional nuclear magnetic resonance techniques, as well as new approaches to combine functional magnetic resonance imaging (fMRI) data with information from other modalities such as positron emission tomography (PET), magnetoencephalography (MEG) and noninvasive optical imaging. The techniques his group has developed to measure physiological and metabolic changes associated with brain activation and cerebrovascular insult are used by research centers and hospitals throughout the world.
CONN is a Matlab-based cross-platform imaging software for the computation, display, and analysis of functional connectivity in fMRI in the resting state and during task.
Arterial spin labeling (ASL), also known as arterial spin tagging, is a magnetic resonance imaging technique used to quantify cerebral blood perfusion by labelling blood water as it flows throughout the brain. ASL specifically refers to magnetic labeling of arterial blood below or in the imaging slab, without the need of gadolinium contrast. A number of ASL schemes are possible, the simplest being flow alternating inversion recovery (FAIR) which requires two acquisitions of identical parameters with the exception of the out-of-slice saturation; the difference in the two images is theoretically only from inflowing spins, and may be considered a 'perfusion map'. The ASL technique was developed by John S. Leigh Jr, John A. Detre, Donald S. Williams, and Alan P. Koretsky in 1992.
Sharlene D. Newman is an American cognitive neuroscientist, executive director of the Alabama Life Research Institute at the University of Alabama (UA), Professor in the Department of Psychology at UA, and an adjunct professor in the Department of Psychological and Brain Sciences at Indiana University.
Functional MRI imaging methods have allowed researchers to combine neurocognitive testing with structural neuroanatomical measures, take into consideration both cognitive and affective paradigms, and subsequently create computer-aided diagnosis techniques and algorithms. Functional MRI has several benefits, such as its non-invasive quality, relatively high spatial resolution, and decent temporal resolution. One particular method used in recent research is resting-state functional magnetic resonance imaging, rs-fMRI. fMRI imaging has been applied to numerous behavioral studies for schizophrenia, the findings of which have hinted toward potential brain regions that govern key characteristics in cognition and affect.
Susan Whitfield-Gabrieli is an American scientist, psychologist/neuroscientist, academic and researcher. She is a professor of psychology, the Founding Director of the Biomedical Imaging Center at Northeastern University, Researcher in the Department of Psychiatry at Massachusetts General Hospital, Harvard Medical School and a Research Affiliate of McGovern Institute for Brain Research at Massachusetts Institute of Technology.
Karla Loreen Miller is an American neuroscientist and professor of biomedical engineering at the University of Oxford. Her research investigates the development of neuroimaging techniques, with a particular focus on Magnetic Resonance Imaging (MRI), neuroimaging, diffusion MRI and functional magnetic resonance imaging. She was elected a Fellow of the International Society for Magnetic Resonance in Medicine in 2016.
Alan Anticevic is a Croatian neuroscientist known for his contributions to the fields of cognitive neuroscience, computational psychiatry, and neuroimaging studies of severe psychiatric illnesses.
